Understanding Consumer Acceptance of AI-Based Recommendation Systems in Online Retail: An Investigation among Digital Consumers in Uttarakhand

Abstract

Artificial Intelligence (AI) has significantly transformed electronic commerce by enabling personalized recommendation systems that predict consumer preferences and deliver customized product suggestions. Recommendation engines are now central to digital retail platforms because they improve product discovery, increase customer engagement, and enhance organizational profitability. Despite the rapid adoption of AI-enabled shopping platforms across India, little empirical evidence exists regarding consumer acceptance of AI recommendation systems in semi-urban and emerging digital markets such as Uttarakhand. Consumers in this region display diverse purchasing behavior influenced by technological awareness, trust, privacy concerns, internet accessibility, and perceived usefulness of AI-driven services.

The present study investigates the factors influencing consumer acceptance of AI-based recommendation systems among digital consumers in Uttarakhand. The study adopts an empirical quantitative research design involving structured questionnaires administered to online shoppers from Dehradun, Haridwar, Roorkee, Haldwani, Rishikesh, Rudrapur and nearby towns. Key variables examined include perceived usefulness, perceived ease of use, trust in AI recommendations, privacy concerns, personalization quality, and purchase intention. Statistical analysis demonstrates that usefulness, personalization, and trust positively influence consumer acceptance, whereas privacy concerns exhibit a comparatively weaker negative effect. The findings contribute to both academic understanding and managerial practice by highlighting how AI recommendation systems can improve customer satisfaction when transparency and data protection measures are emphasized.